Our latent-space mistrust score uses cosine similarity for pairwise comparison of a test sample's latent-space embedding with those of training data. This could be computationally intensive, especially for large datasets. We reduce computation costs, memory overhead and latency by using a subset of training data (coreset) instead of the full trainset. The coreset is extracted simply by randomly sampling a fraction from every class in the trainset, unlike complicated strategies used in other works [1]}.
We see from Table REF  that just 2% of the trainset is sufficient to achieve performance similar to the full trainset. In fact, for our clinical EEG task, we use just 2% of the training samples for all experiments and reach the SOTA.
<TABLE>